class_docstr: QA dataset with common knowledge facts.
docstr: Returns SplitGenerators.
docstr_tok: ["Returns","SplitGenerators","."]
code: def _split_generators(self, dl_manager):
      """Returns SplitGenerators."""
  
      download_url = "https://s3-us-west-2.amazonaws.com/ai2-website/data/OpenBookQA-V1-Sep2018.zip"
      dl_dir = dl_manager.download_and_extract(download_url)
      data_dir = os.path.join(
          dl_dir, "OpenBookQA-V1-Sep2018/Data/Additional"
      )
      return [
          tfds.core.SplitGenerator(
              name=tfds.Split.TRAIN,
              # These kwargs will be passed to _generate_examples
              gen_kwargs={
                  "data_dir": data_dir,
                  "filepath": os.path.join(
                      data_dir, "train_complete.jsonl"
                  ),
              },
          ),
          tfds.core.SplitGenerator(
              name=tfds.Split.VALIDATION,
              # These kwargs will be passed to _generate_examples
              gen_kwargs={
                  "data_dir": data_dir,
                  "filepath": os.path.join(
                      data_dir, "dev_complete.jsonl"
                  ),
              },
          ),
          tfds.core.SplitGenerator(
              name=tfds.Split.TEST,
              # These kwargs will be passed to _generate_examples
              gen_kwargs={
                  "data_dir": data_dir,
                  "filepath": os.path.join(
                      data_dir, "test_complete.jsonl"
                  ),
              },
          ),
      ]
  
file_path: datasets/tensorflow_datasets/datasets/openbookqa/openbookqa_dataset_builder.py
imports: import json
  import os
  from etils import epath
  import numpy
  import tensorflow_datasets.public_api
label_desc: Use image node_id 2 for calling the Builder obj's underlying member method code with example usage: obj._split_generators(dl_manager) and returns: list